Semantic Segmentation of human depth images

This project is the consequence of an internal course requirement during my masters. The goal of this project, on its own, is to segment human body parts from depth images. Results from several different U-Net based models are compared in this work, with particular focus on high speed and accuracy.

Provided in this project are functions to obtain and preprocess the training data, train the segmentation graph, and freezes the graph to a protobuf file for later inference in C++.

Further development of this work (not included in this repository) focuses on combining the segmented depth maps using KinectFusion to provide a segmented 3D point cloud/mesh. This segmented 3D model of the human body was used in collaboration with some other techniques to provide a view into a patient’s body.

A detailed report on this work can be found in the link below.